Phenotype-centric modeling for rational metabolic engineering - Valderrama-Gómez, Miguel Á.; Savageau, Michael A. (Metabolic engineering, 2022)
Phenotype-centric modeling enables a paradigm shift in the analysis of mechanistic models. It brings the focus to a network's biochemical phenotypes and their relationship with measurable traits (e.g., product yields, system dynamics, signal amplification factors, etc.) and away from computationally intensive simulation-centric modeling. Here, we explore applications of this new modeling strategy in the field of rational metabolic engineering using the amorphadiene biosynthetic network as a case study. This network has previously been studied using a mechanistic model and the simulation-centric strategy, and thus provides an excellent means to compare and contrast results obtained from these two very different strategies. We show that the phenotype-centric strategy, without values for the parameters, not only identifies beneficial intervention strategies obtained with the simulation-centric strategy, but it also provides an understanding of the mechanistic context for the validity of these predictions. Additionally, we propose a set of hypothetical strains with the potential to outperform reported production strains and to enhance the mechanistic understanding of the amorphadiene biosynthetic network. Further, we identify the landscape of possible intervention strategies for the given model. We believe that phenotype-centric modeling can advance the field of rational metabolic engineering by enabling the development of next generation kinetics-based algorithms and methods that do not rely on a priori knowledge of kinetic parameters but allow a structured, global analysis of system design in the parameter space. •Phenotype-centric modeling enables a paradigm shift in mechanistic modeling.•Phenotypic repertoire identified without a priori knowledge of kinetic parameters.•The phenotypic repertoire can be filtered to identify top-performing phenotypes.•Phenotype of the currently parameterized amorphadiene network is a top performer.•A global landscape of design strategies offers new prospects for strain development.